A Dynamic Regional-Aggregation-Based Heterogeneous Graph Neural Network for Traffic Prediction
Traffic flow prediction, crucial for intelligent transportation systems, has seen advancements with graph neural networks (GNNs), yet existing methods often fail to distinguish between the importance of different intersections. These methods usually model all intersections uniformly, overlooking sig...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/9/1458 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850032142966325248 |
|---|---|
| author | Xiangting Liu Chengyuan Qian Xueyang Zhao |
| author_facet | Xiangting Liu Chengyuan Qian Xueyang Zhao |
| author_sort | Xiangting Liu |
| collection | DOAJ |
| description | Traffic flow prediction, crucial for intelligent transportation systems, has seen advancements with graph neural networks (GNNs), yet existing methods often fail to distinguish between the importance of different intersections. These methods usually model all intersections uniformly, overlooking significant differences in traffic flow characteristics and influence ranges between ordinary and important nodes. To tackle this, this study introduces a dynamic regional-aggregation-based heterogeneous graph neural network (DR-HGNN). This model categorizes intersections into two types—ordinary and important—to apply tailored feature aggregation strategies. Ordinary intersections aggregate features based on local neighborhood information, whereas important intersections utilize deeper neighborhood diffusion and multi-hop dependencies to capture broader traffic influences. The DR-HGNN model also employs a dynamic graph structure to reflect temporal changes in traffic flows, alongside an attention mechanism for adaptive regional feature aggregation, enhancing the identification of critical traffic nodes. Demonstrating its efficacy, the DR-HGNN achieved 19.2% and 15.4% improvements in the RMSE over 50 min predictions in the METR-LA and PEMS-BAY datasets, respectively, offering a more precise prediction method for traffic management. |
| format | Article |
| id | doaj-art-e17971687db7454fb4da787c4b4581f6 |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-e17971687db7454fb4da787c4b4581f62025-08-20T02:58:44ZengMDPI AGMathematics2227-73902025-04-01139145810.3390/math13091458A Dynamic Regional-Aggregation-Based Heterogeneous Graph Neural Network for Traffic PredictionXiangting Liu0Chengyuan Qian1Xueyang Zhao2School of International Education, Guangdong University of Technology, Guangzhou 511495, ChinaSchool of Mechanical and Energy Engineering, Tongji University, Shanghai 201804, ChinaDepartment of Mathematics and Physics, Harbin Institute of Petroleum, Harbin 150028, ChinaTraffic flow prediction, crucial for intelligent transportation systems, has seen advancements with graph neural networks (GNNs), yet existing methods often fail to distinguish between the importance of different intersections. These methods usually model all intersections uniformly, overlooking significant differences in traffic flow characteristics and influence ranges between ordinary and important nodes. To tackle this, this study introduces a dynamic regional-aggregation-based heterogeneous graph neural network (DR-HGNN). This model categorizes intersections into two types—ordinary and important—to apply tailored feature aggregation strategies. Ordinary intersections aggregate features based on local neighborhood information, whereas important intersections utilize deeper neighborhood diffusion and multi-hop dependencies to capture broader traffic influences. The DR-HGNN model also employs a dynamic graph structure to reflect temporal changes in traffic flows, alongside an attention mechanism for adaptive regional feature aggregation, enhancing the identification of critical traffic nodes. Demonstrating its efficacy, the DR-HGNN achieved 19.2% and 15.4% improvements in the RMSE over 50 min predictions in the METR-LA and PEMS-BAY datasets, respectively, offering a more precise prediction method for traffic management.https://www.mdpi.com/2227-7390/13/9/1458traffic flow predictiondynamic heterogeneous graph neural networkregional aggregationattention mechanismspatiotemporal dependencies |
| spellingShingle | Xiangting Liu Chengyuan Qian Xueyang Zhao A Dynamic Regional-Aggregation-Based Heterogeneous Graph Neural Network for Traffic Prediction Mathematics traffic flow prediction dynamic heterogeneous graph neural network regional aggregation attention mechanism spatiotemporal dependencies |
| title | A Dynamic Regional-Aggregation-Based Heterogeneous Graph Neural Network for Traffic Prediction |
| title_full | A Dynamic Regional-Aggregation-Based Heterogeneous Graph Neural Network for Traffic Prediction |
| title_fullStr | A Dynamic Regional-Aggregation-Based Heterogeneous Graph Neural Network for Traffic Prediction |
| title_full_unstemmed | A Dynamic Regional-Aggregation-Based Heterogeneous Graph Neural Network for Traffic Prediction |
| title_short | A Dynamic Regional-Aggregation-Based Heterogeneous Graph Neural Network for Traffic Prediction |
| title_sort | dynamic regional aggregation based heterogeneous graph neural network for traffic prediction |
| topic | traffic flow prediction dynamic heterogeneous graph neural network regional aggregation attention mechanism spatiotemporal dependencies |
| url | https://www.mdpi.com/2227-7390/13/9/1458 |
| work_keys_str_mv | AT xiangtingliu adynamicregionalaggregationbasedheterogeneousgraphneuralnetworkfortrafficprediction AT chengyuanqian adynamicregionalaggregationbasedheterogeneousgraphneuralnetworkfortrafficprediction AT xueyangzhao adynamicregionalaggregationbasedheterogeneousgraphneuralnetworkfortrafficprediction AT xiangtingliu dynamicregionalaggregationbasedheterogeneousgraphneuralnetworkfortrafficprediction AT chengyuanqian dynamicregionalaggregationbasedheterogeneousgraphneuralnetworkfortrafficprediction AT xueyangzhao dynamicregionalaggregationbasedheterogeneousgraphneuralnetworkfortrafficprediction |